Nutritional Epidemiology in the Genomic Age - UNC NRI · Nutritional Epidemiology in the Genomic...
Transcript of Nutritional Epidemiology in the Genomic Age - UNC NRI · Nutritional Epidemiology in the Genomic...
Nutritional Epidemiology in the Genomic Age
Saroja Voruganti, PhD Assistant Professor
Department of Nutrition and UNC Nutrition Research Institute
Learning objectives
� Types of genetic association approaches and their relevance to nutritional research
� Models used to analyze associations of genetic variants with disease phenotypes
and gene-nutrient interactions
Environmental (diet) Factors
Genetic Factors
Gene X
Gene
Gene X
Environment (diet)
Research question??
� What are the genes that affect nutrient
metabolism?
Or
� How do our nutrient or diet intake affect the expression of a gene?
Nutritional Epidemiological approaches
� Correlation studies � Special exposure groups
� Migrant studies
� Case control and cohort studies
� Controlled trials
Willett W. Overview of Nutritional Epidemiology gDOI:10.1093/acprof:oso/9780199754038.003.0001
Genetic Epidemiological approaches
• Case studies • Cross sectional studies
• Cohort • Case-control
• Family-based, twin and trio studies • Clinical trials
� Phenotype ◦ Is it properly defined?
◦ Is it genetically controlled? ◦ Is it likely to have effects mediated by a given
environmental factor?
� Genotype ◦ Does it show evidence for linkage, association,
or interaction? ◦ Are the SNPs in promoter, intron or exon?
◦ What do we know about the SNP?
Defining the phenotype and genotype
Biological samples and other data
� Nutrient data � DNA (from blood, saliva and other tissues)-
genotyping or sequencing, epigenetics � RNA (from blood or tissues) – transcriptomic profiles
� Blood (serum or plasma) –biochemical variables, metabolomics, proteomics, lipidomics, etc
� Urine – biochemical variables, metabolomics � Data such as age, sex, BMI, etc
� Medical history � Environmental factors
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How can variants affect phenotypes?
• silent; most variants have no effect
• Altered protein sequence – nonsynonymous, nonsense, splice, stop loss
• Altered RNA processing
• Altered RNA expression (regulatory)
• Other
Approaches to genotyping
• Candidate genes: - genotype only markers in genes
potentially related to the trait
• Genome screen: - genotype anonymous markers
spanning the genome at regular intervals
Genotyping
1-10
10-500
500-500,000
500,000-2M
Candidate genes (Taqman)
(Batches)SNaP shot; SNPlex; Sequenom Mass Array
Illumina Golden Gate; Custom SNP chips
GWAS; Illumina; Affymetrix
Adapted from Edenberg and Liu. Cold Spring Harbor Laboratory Press; 2009
Terminology
Ardlie, Kruglyak & Seielstad Nature Reviews Genetics, 2002; 3, 299-309
Linkage Disequilibrium
Haplotype
Hardy-Weinberg Principle
Design:Family
Phenotypes:Quantitative /Qualitative Phenotypes Markers:STR or SNP
Information:Segregation (IBD)
Genome-wide Approaches
Design:Case-Control / Family
Phenotypes:Quantitative /Qualitative Phenotypes
Markers:STR or SNP
Information:Linkage Disequilibrium (IBS)
ASSOCIATION
LINKAGE
• Homozygote (AA) – • 2 copies of major allele (‘common’)
• Heterozygote (Aa) – • 1 copy of major allele and 1 of minor allele
• Homozygote (aa) – • 2 copies of minor allele (‘variant’)
Modeling in genetic epidemiology
Modeling in genetic epidemiology
� the mode of inheritance � 1. Additive
� 2. Dominant � 3. Recessive
� With family data/ pedigrees – assess mode of inheritance
� BUT….Can’t be done in studies of unrelated individuals, complex with common traits
� Statistical power is reduced if you specify the wrong model
Recoding for alternative models
Additive Dominant
Recessive
AA 0 0 0
AG 1 1 0
GG 2 1 1
� Dominant model combines AG+GG ◦ Only need one copy
of rare allele for disease ◦ Used when frequency
of GG is low
� Recessive model combines AA & AG ◦ Have to have 2 copies
of rare allele (G) for disease ◦ Rarely used
Power calculation
� http://bmcgenet.biomedcentral.com/articles/10.1186/1471-2156-9-36 (PGA-Power
calculator for case-control genetic association studies)
� http://www.biostat.ucsf.edu/sampsize.html
� http://homepage.stat.uiowa.edu/~rlenth/Power/
� http://biomath.info/power/
� http://pngu.mgh.harvard.edu/~purcell/gpc/
Genetic association tools
� http://goldenhelix.com/products/SNP_Variation/index.html
� http://genemapping.org/online material/online-resources
� http://www.broadinstitute.org/scientific-community/software?criteria=Genetic
%20Analysis
� http://bmcresnotes.biomedcentral.com/articles/10.1186/1756-0500-4-158
� http://www.biostat.wustl.edu/genetics/geneticssoft/SoftwareList.htm
� http://www.stats.ox.ac.uk/~marchini/software/gwas/gwas.html
� http://www.disgenet.org/web/DisGeNET/menu;jsessionid=16q535dpjpour10rfwtudqdjt
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� http://biostats.usc.edu/software � http://pngu.mgh.harvard.edu/~purcell/
plink/
Genetic association tools
Websites
� UCSC Genome Browser – https://genome.ucsc.edu/cgi-bin/hgGateway
� NCBI Map Viewer
http://www.ncbi.nlm.nih.gov/projects/mapview/
Others
� Online Mendelian Inheritance in Man http://www.ncbi.nlm.nih.gov/omim
� Gene
http://www.ncbi.nlm.nih.gov/gene
� Gene Cards
http://www.genecards.org/
� HAPMAP http://hapmap.ncbi.nlm.nih.gov/
� ENCODE
https://genome.ucsc.edu/ENCODE/
Or
http://www.genome.gov/10005107
Others
LocusZoom
• http://locuszoom.sph.umich.edu/locuszoom/
• a tool to plot regional association results from genome-wide association scans or candidate gene studies. This is Version 1.1
Effects of gene-environment interaction on phenotypes
What is Gene-Environment Interaction (GEI)?
� Distinct effects of an environmental factor in individuals with different
genotypes
or � Distinct effects of a genotype in two
different environments
Gene-Environment Interaction
Kraft and Hunter.. 2005. Philosophical Transactions of the Royal Society B.
Gene by nutrient interaction effects on metabolic disease
Model I -Phenylketonuria
Model 1
genotype
disease risk factor
Mutation in phenylalanine hydroxylase
PKU
High levels of phenylalanine in blood
Genotype increases the expression of risk factor
Model II – Xeroderma Pigmentosum
Model 2
genotype
disease risk factor
Mutations in nucleotide excision repair enzymes
Skin cancer
UV radiation
Genotype excarbates the effect of the risk factor
Model III- Porphyria variegate
genotype
disease risk factor
Model 3
The risk factor excarbates the effect of the genotype
Mutation in PPOX gene
Skin problems Barbiturates
and seizure medications
Model IV- alpha-1 antitrypsin deficiency
Genotype and risk factor each influence the risk by themselves
Model 4
genotype
disease
risk factor
Model 4
Mutation in SERPINA1
Lung disease
Smoke or pollutants
Model V-G6PD deficiency
Both genotype and risk factor are required to raise the risk
Model 5
genotype
disease
risk factor
Mutation in glucose 6 phosphate dehydrogenase
Hemolytic anemia
Fava bean consumption
Nutrigenetic differences
• Most of them may have been inherited from our ancestors
• Genetic variation affects food tolerances among populations
• Nutritional environments seem to be the major determinants of human variation evolution
• Populations vary in their requirements for foods and response to diet
SNP by Environment Interaction
Main effects model:
� T(E) = βM0i+ βM
1iE + βM2iSNP
Interaction effects model:
� T(E)=βI0i+ βI
1iE+βI2iSNP+ βI
3iSNPxE � T(E) = variation in the phenotype T,
� βM = coefficients related to main effects,
� βM = coefficients related to interaction effects,
� E = environmental factor,
� SNP is usually coded as 0,1 and 2 based on the number of rare alleles, and
� SNP x E= interaction term
Example
Serum uric acid
Guanosine mono phosphate (GMP)
Inosine mono phosphate (IMP)
Allantoin
Xanthine
Uric acid
Xanthine oxidase
Uricase Humans and some higher primates
Adenosine mono phosphate (AMP)
• Is a genetic study of CVD risk in American Indians
• It is the genetic component of the Strong Heart Study
started in 1998
• More than 3800 members from multigenerational
families enrolled from three centers located in Arizona,
Dakotas and Oklahoma
Strong Heart Family study (SHFS) [PI: Dr. Shelley Cole]
Viva La Familia [PI: Dr. Nancy Butte]
• Overweight/obese Hispanic children aged 4-19 years were
recruited
• Some unique phenotypes such as calorimetry
measurements, physical activity and energy
expenditure have been collected
• Genome-wide SNP, exome and metabolomic data available
Descriptives
SHFS VFS Age 39.50 ± 17 11.0 ± 4 Serum uric acid (mg/dl)
5.1 ± 1.5 5.2 ± 1.7
Hyperuricemia (%) 17 25 Sugars intake (% of total calories)
16.3 22
Heritability (%) 46 45
Dietary
variable
All Arizona Dakotas Oklahoma
β (SE) P value β (SE) P value β (SE) P value β (SE) P value Alcohol intake
-0.219 (0.03)
5.6 x 10-10
-0.187 (0.04)
4.1 x 10-6 -0.175 (0.04)
9.0 x 10-5 -0.192 (0.04)
3.2 x 10-6
Protein intake
0.0007 (0.0002)
0.0004 0.0004 (0.0002)
0.16 0.0008 (0.0003)
1.8 x 10-2 0.0012 (0.0004)
0.008
Simple sugars
0.0003 (0.0002)
0.82 -0.0009 (0.0003)
0.72 0.0014 (0.0003)
0.65 0.0007 (0.0004)
0. 768
SLC2A9* SNPs and serum uric acid levels (SHFS)**
* Solute carrier family 2, member 9 ** Voruganti et al., EJHG 2014
Genetic influence on serum uric acid and clearance
Serum uric acid on chromosome 4
Uric acid clearance on chromosome 19
Locus Zoom plot showing the most significant SNPs on chr 19q13
Mendelian randomization
SLC2A9 variants (Instrument)
Serum uric acid (risk factor)
Chronic kidney disease (Outcome) Age, sex, age*sex
(Confounders)
Association of SUA genetic risk score with kidney function markers
Dietary Factors affecting serum uric acid levels
Fructose [Carbonated beverages, most canned products, honey]
High-purine foods and amino acids [Organ meats such as liver, spleen, heart etc]
Alcohol
ATP depletion
Competition with uric acid for the same transporter (SLC2A9)
AMP, GMP or IMP
Dehydration
Hyperuricemia
Uric acid and Fructose
� Uric acid is a byproduct of fructose degradation and shares a transporter with
fructose (GLUT9/SLC2A9)
� Fructokinase is poorly regulated and phosphorylates fructose rapidly
� Fructose upregulates its transporter GLUT5 as well as fructokinase
� Serum uric acid increases rapidly after ingestion of fructose
� Fructose interferes with uric acid excretion
Genotype-specific differences in SUA/added sugars
Minor allele shown next to the SNP in parantheses; added sugars are shown as percent of calories
Population-specific effects of SLC17A1 on serum uric acid
concentrations during a fructose load
Dalbeth et al. Ann Rheum Dis. 2014; 73: 313-314
Effect of ABCG2 genotype on serum uric acid concentrations during a
fructose load
Dalbeth et al. Arthritis Research and Therapy. 2014; 16:R34
Genotype- and population-specific effects of fructose on uric acid
related genes
Select 20 samples each from Caucasian, Hispanic and African American populations matched for age, sex and body weight
They will undergo a fructose challenge study
The 60 individuals will come to NRI. After collection of fasting blood and urine sample, they will be given a fructose drink.
Blood will be collected at regular intervals. Uric acid will be measured in serum and urine
We expect to find significant differences in the response to fructose challenge based on genotype and population
We will genotype 100 top uric acid associated SNPs and also measure gene expression of uric acid related genes
• Voruganti Lab
• Participants of all studies
• NIH Grants NIH R01 DK092238, NIDDK P01 DK056350
• UNC NRI faculty and staff
• Collaborators Texas Biomedical Research Institute, San Antonio Baylor College of Medicine, Houston MURDOCK Study, Duke University